{"id":"W6889000575","doi":"10.25384/sage.11848065.v1","title":"Interview_profile_appendix_online_supp – Supplemental material for Tracing Discretion in Planning and Land-Use Outcomes: Perspectives from Toronto, Canada","year":2020,"lang":"en","type":"article","venue":"Figshare","topic":"Urban, Neighborhood, and Segregation Studies","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Discretion; Tracing; Strategic planning; Work (physics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00511659,0.0008078665,0.0006873456,0.003397011,0.01326467,0.002792553,0.001980081,0.002232092,0.1732029],"category_scores_gemma":[0.01378352,0.000905299,0.0006138462,0.009330416,0.001400993,0.001606461,0.002181204,0.003693542,0.02172657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.06337357,"about_ca_system_score_gemma":0.1245774,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9920785,"about_ca_topic_score_gemma":0.99741,"domain_scores_codex":[0.996769,0.0005141724,0.0001778187,0.000198151,0.001139154,0.00120177],"domain_scores_gemma":[0.9779279,0.004584159,0.0005147838,0.000636364,0.01318132,0.003155397],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002242718,0.00003976326,0.004951777,0.0001001378,0.000003900387,0.00006527842,0.005493562,0.00009429237,0.0001000938,0.00113889,0.9795979,0.008391903],"study_design_scores_gemma":[0.00007310208,0.00003908777,0.109632,0.000546816,0.00002111505,0.0000722234,0.09006034,0.000384078,0.0003069072,0.00118741,0.7975397,0.0001372447],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.03661615,0.002004906,0.003324008,0.05436155,0.002000307,0.007261991,0.655439,0.0004650016,0.2385271],"genre_scores_gemma":[0.1807611,0.005953354,0.01232625,0.02462404,0.0007658586,0.0211896,0.1537835,0.0006917319,0.5999047],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1732029,"threshold_uncertainty_score":0.5794216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06822882930093156,"score_gpt":0.3279426535958003,"score_spread":0.2597138242948688,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}